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Static GGUF files of FallenMerick's MN-Violet-Lotus-12B in sizes from 4.9 GB to 13.1 GB. For people who run a 12B model on their own computer.
MN-Violet-Lotus-12B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download mradermacher/MN-Violet-Lotus-12B-GGUF
This repository holds GGUF files that mradermacher produced from FallenMerick's MN-Violet-Lotus-12B model. Each file trades size for quality, so you choose one that fits your memory. The model has 12.2B parameters and lists English. The card does not say what the base model was built to do.
It suits people who want a 12B model running on their own computer in a smaller file than the original weights.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Home use | Local AI users | Run a 12B model on their own computer | Load the Q4_K_M file of 7.6 GB in a GGUF app |
| Software | Developers | Test a quantized model on local hardware | Compare Q4_K_S and Q8_0 for speed and quality |
| Research | Quantization researchers | Study how quality changes between sizes | Run one prompt set against several quant types |
If you want the original full-precision weights, you can skip it and use the model it was made from.
You open the files in a program that reads GGUF, on Windows, macOS or Linux. The card points to TheBloke's READMEs for how to use GGUF files.
| Use case | Who | What they get back |
|---|---|---|
| Local chat | Home users | A model that answers on their own machine |
| Size testing | Developers | The smallest file that still works for them |
| Imatrix quants | Enthusiasts | Weighted versions in the card's other repository |
You need Python for the download step, a program that loads GGUF files, and enough disk space for the file you choose. Hugging Face estimates about 6.1 GB at 4-bit.
pip install -U huggingface_hub hf download mradermacher/MN-Violet-Lotus-12B-GGUF
Choose a quant from the list, load it in your GGUF program and chat. The repository holds many files, so check the sizes before you pull everything. The card says IQ types are often better than non-IQ types of similar size.
| Tool | What it does |
|---|---|
| Q2_K, 4.9 GB | The smallest file |
| Q3_K_M, 6.2 GB | A small file that the card says has lower quality |
| Q4_0_4_4, 7.2 GB | The card says it is fast on ARM chips but low in quality |
| Q4_K_S, 7.2 GB | The card marks it as fast and recommended |
| Q4_K_M, 7.6 GB | Also fast and recommended |
| Q6_K, 10.2 GB | The card rates its quality as high |
| Q8_0, 13.1 GB | Fast, and the best quality in the list |
You pick the Q4_K_M file at 7.6 GB, load it in a GGUF program and send a message. The model answers in text from your own machine.
If you are new to this, a GGUF file is a model squeezed into a smaller size so it can run at home. A first small step is to pick the Q4_K_M file, which the card recommends.
If you know the field, these are static quants, listed by size and not strictly by quality. The card says weighted imatrix quants live in a separate i1 repository from the same author.
The files range from 4.9 GB to 13.1 GB. The card links to a graph and a write-up that compare lower-quality quant types.
Static GGUF files of FallenMerick's MN-Violet-Lotus-12B in sizes from 4.9 GB to 13.1 GB. For people who run a 12B model on their own computer.
Made by mradermacher on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.
What it is: This repository holds GGUF files that mradermacher produced from FallenMerick's MN-Violet-Lotus-12B model. Each file trades size for quality, so you choose one that fits your memory. The model has 12.2B parameters and lists English. The card does not say what the base model was built to do.
Who it suits: It suits people who want a 12B model running on their own computer in a smaller file than the original weights.
Industry | Who uses it | How they use it | Example
Home use | Local AI users | Run a 12B model on their own computer | Load the Q4_K_M file of 7.6 GB in a GGUF app
Software | Developers | Test a quantized model on local hardware | Compare Q4_K_S and Q8_0 for speed and quality
Research | Quantization researchers | Study how quality changes between sizes | Run one prompt set against several quant types
If you want the original full-precision weights, you can skip it and use the model it was made from.
Where to use it: You open the files in a program that reads GGUF, on Windows, macOS or Linux. The card points to TheBloke's READMEs for how to use GGUF files.
Use case | Who | What they get back
Local chat | Home users | A model that answers on their own machine
Size testing | Developers | The smallest file that still works for them
Imatrix quants | Enthusiasts | Weighted versions in the card's other repository
How to set it up: You need Python for the download step, a program that loads GGUF files, and enough disk space for the file you choose. Hugging Face estimates about 6.1 GB at 4-bit.
pip install -U huggingface_hub
hf download mradermacher/MN-Violet-Lotus-12B-GGUF
How to use it: Choose a quant from the list, load it in your GGUF program and chat. The repository holds many files, so check the sizes before you pull everything. The card says IQ types are often better than non-IQ types of similar size.
Tool | What it does
Q2_K, 4.9 GB | The smallest file
Q3_K_M, 6.2 GB | A small file that the card says has lower quality
Q4_0_4_4, 7.2 GB | The card says it is fast on ARM chips but low in quality
Q4_K_S, 7.2 GB | The card marks it as fast and recommended
Q4_K_M, 7.6 GB | Also fast and recommended
Q6_K, 10.2 GB | The card rates its quality as high
Q8_0, 13.1 GB | Fast, and the best quality in the list
Example: You pick the Q4_K_M file at 7.6 GB, load it in a GGUF program and send a message. The model answers in text from your own machine.
For newcomers and experts: If you are new to this, a GGUF file is a model squeezed into a smaller size so it can run at home. A first small step is to pick the Q4_K_M file, which the card recommends.
If you know the field, these are static quants, listed by size and not strictly by quality. The card says weighted imatrix quants live in a separate i1 repository from the same author.
FallenMerick's MN-Violet-Lotus-12B
Static GGUF quantization
Files in many sizes
You pick one that fits your memory
Your GGUF program runs it
Good to know: The files range from 4.9 GB to 13.1 GB. The card links to a graph and a write-up that compare lower-quality quant types.
Title: File size by quant type, in GB
Q2_K: 4.9 GB
Q3_K_M: 6.2 GB
IQ4_XS: 6.9 GB
Q4_K_S: 7.2 GB
Q4_K_M: 7.6 GB
Q5_K_M: 8.8 GB
Q6_K: 10.2 GB
Q8_0: 13.1 GB
Download its files with Hugging Face's command line:
pip install -U huggingface_hub
hf download mradermacher/MN-Violet-Lotus-12B-GGUF
Then follow the usage in its model card. Nothing in this kit runs by itself.
README.md 5 KB
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